This is Chinese version, if you have not seen the blog (in English) yet, go here: https://developmentseed.org/blog/2018/01/11/label-maker/

Label Maker is a python library to help in extracting insight from satellite imagery. Label Maker creates machine-learning-ready training data for most popular ML frameworks, including Keras, TensorFlow, and MXNet. It pulls data from OpenStreetMap and combines that with imagery sources like Mapbox or Digital Globe to create a single file for use in training machine learning algorithms.

It doesn’t matter which part of the world you are living now, very diverse tree species are planted around the urban area we live. Trees in the urban areas have many functions, for example, trees provide habitats for wildlife, clean air and water, provide significant health and social benefits, and also improve property value too. Wake up in a beautiful morning that birds are singing outside your apartment because you have many beautiful trees grow outside of your space. How awesome is that!

However, tree planting, survey, and species identification require an enormous amount of work that literally took generations and years of inputs and care. What if we could identify tree species from satellite imagery, how much faster and how well we could get tree species identified and also tell their geolocations as well.

A city has its own tree selection and planting plan, but homeowners have their own tree preference, which the identification work a bit complicated, though.

(Photo from Google Earth Pro June 2010 in Chicago area)

It’s hard to tell now how many tree species are planted in above image. But we could (zoom in and) tell these trees actually have a slightly different shape of tree crown, color, and texture. From here I only need to have a valid dataset basically tell me what tree I am looking at now, which is a tree survey and trees geolocation records from the city. I will be able to teach a computer to select similar features for the species I’m interested in identifying.

These are Green Ash trees (I marked as green dots here).

These are Littleleaf Linden, they are marked as orange dots.

Let me run a Caffe deep learning model (it’s one of the neural networks and also known as artificial intelligence model) for an image classification on these two species, and see if the computer could separate these two species from my training and test datasets.

Great news that the model could actually tell the differences between these two species. I run the model for 300 epochs (runs) from learning rate 0.01 to 0.001 on about 200 images for two species. 75% went to train the model and 25% for testing. The result is not bad that we have around 90% of accuracy (orange line) and less than 0.1 loss on the training dataset.

I threw a random test image to the model (a green ash screenshot in this case) and it tells the result.

I will be working on identifying other 20 trees species and their geolocations next time.

Let’s get some answer what trees are planted in Chicago area and how it related to the property value (an interesting question to ask), and also what ecological benefits and functions these tree are providing (leave this to urban ecologist if my cloud computer could identify the species)? Check my future work ;-).

The following work was done by me and Dr. Shay Strong, while I was a data engineer consultant under the supervision of Dr. Strong at OmniEarth Inc. All the work IP rights belong to OmniEarth. Dr Strong is the Chief Data Scientist at OmniEarth Inc.

After obtaining 0.4 m resolution satellite imagery of the wildfire damage in Gatlinburg and Pigeon Forge from Digital Global, OmniEarth Inc created an artificial intelligence (AI) model that was able to assess and identify the property damage due to the wildfire. This AI model will also be able to more rapidly evaluate and identify areas of damage from natural disasters from similar issues in the future.

With assistance from increasing cloud computing power and a better understanding of computer vision, more and more AI technology is helping us detect information from trillions of photos we produce daily.计算机图像识别和云计算能力的提升，使得我们能够借助人工智能模型获取数以万计甚至亿计的照片地图等图片中获取有用的信息。

Before diving into the AI model behind the wildfire damage, in this case, we only want to identify the differences between fire-damaged buildings and intact buildings. We have two options: (1), we could spend hours and hours browsing through the satellite images and manually separate the damaged and intact buildings or (2) develop an AI model to automatically identify the damaged area with a tolerable error. For the first option, it would easily take a geospatial technician more than 20 hours to identify the damaged area among the 50,000 acres of satellite imagery. The second option poses a more viable and sustainable solution in that the AI model could automatically identify the damaged area/buildings less than 1 hour over the same area. This is accomplished by image classification in AI, using convolutional neural networks (CNN) specifically, because CNN works better than other neural network algorithms for object detection and recognition from images.

Artificial intelligence/neural networks are a family of machine learning models that are inspired by biological neurons of our human brain. First conceived in the 1960s, but the first breakthrough was Geoffrey Hinton’s work published in the mid-2000s. While our human eyes work like a camera seeing the ‘picture,’ our brain will process it and be able to construct the objects we see through the shape, color, and texture of the objects. The information of “seeing” and “recognition” is passing through our biological neurons from our eyes to our brain. The AI model we created works in a similar way. The imagery is passed through the artificial neural network, and objects that have been taught to the neural network are identified with certain accuracy. In this case, we taught the network to learn the difference between burnt and not-burnt structures in Gatlinburg and Pigeon Forge, TN.

2. How did we build the AI model

We broke down the wildfire damage mapping process into four parts (Fig 1). First, we obtained the 0.4m resolution satellite images from Digital Globe (https://www.digitalglobe.com/). We created a training and a testing dataset of 300 small images chips (as shown in Fig 3, A and B) that contained both burnt and intact buildings, 2/3 of which go to train the AI model, CNN model in this case, and 1/3 of them are for test the model. Ideally, the more training data used to represent the burnt and non-burnt structures are ideal for training the network to understand all the variations and orientations of a burnt building. The sample set of 300 is on the statistically small side, but useful for testing capability and evaluating preliminary performance.

Fig 3(A). A burnt building

Fig3(B). Intact buildings

Our AI model was a CNN model that built upon Theano (GPU backend) (http://deeplearning.net/software/theano/). Theano was created by the Machine Learning group at the University of Montreal, led by Yoshua Bengio, who is one of the pioneers behind artificial neural networks. Theano is a Python library that lets you define and evaluate mathematical expressions with vectors and matrices. As a human, you can imagine our daily decision-making is based on the matrices of perceived information as well, e.g. which car you want to buy. The AI model helps us to identify which image pixels and patterns are fundamentally different between burnt and intact buildings, similar to how people give a different weight or score to the car brand, model, and color they want to buy. Computers are great at calculating matrices, and Theano brings it to next level because it calculates multiple matrices in parallel, and so speeds up the whole calculation tremendously. Theano has no particular neural network built-in, so we use Keras on top of Theano. Keras allows us to build an AI model with a minimalist design on training layers of a neural network and run it more efficiently.

Our AI model was run on AWS EC2 with a g2.2xlarge instance type. We set the learning rate (lr) to 0.01.. A smaller learning rate will force the network to learn more slowly but may also lead to optimal classification convergence, especially in cluttered scenes where a large amount of object confusion can occur. In the end, our AI model with came out with 97% of accuracy, less than 0.3 loss over three runs within a minute, and it took less than 20 minutes to run on our 3.2G satellite images.

Fig 4. (A). using OmniEarth parcel level burnt and intact buildings layout on top of the imagery.

Fig 4 (B). The burnt impact (red color) on top of the Great Smoky Mountains from late Nov. to early Dec 2016.

Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. A lot of currently available classification approaches are not suitable to handle high-resolution imagery data with inherent high variability in geometry and collection times. However, OmniEarth is a startup company that is passionate about the business of science and scaling quantifiable solutions to meet the world’s growing need for actionable information.

I’ve been working on image classification and segmentation quite a lot recently, and totally in love with GPU big data processing. If you wanna process data that at gigabyte (G) level data definitely look into start a GPU AWS instance 最近我的工作接触了很多图像分类，和图像分割的内容，感觉自己太爱gpu图像分析的世界：太神速了。如果你现在处理的数据已经达到G级别了，我觉得你还是应该开一个亚马逊的ami（亚马逊的深度学习平台／机器）

It is not free, though. You definitely would start with AWS free tier, but I normally use their g or p machines. For example, if I use g2.2 x large, I will be charged about $0.65 per hour. for more information, go here. It charges by how much you use and if you are new to deep learning and just wanna run some case studies, I think it worths more than building your own GPU machine or buy a new pc with super GPU.

What do you wanna do with the AWS machine? Do you wanna learn just some basic machine learning stuffs that you only need to process megabyte (?M) level csv/txt data file you could just use your personal computer. A personal computer is fast enough though days. 你想拿这个亚马逊深度学习平台来做什么？如果只是用来处理几兆几十兆的数据的话，那还是没有必要开一个，现在的个人电脑那么快完全可以处理这些数据了。

As I mentioned above, if you wanna process images or data that above some certain level your personal computer could not handle. Think about how much you wanna spend on the data processing. Again, evaluate your situation, needs and do some research. 但是，如果你的数据量已经是在几百兆或者g级别的，当然还是很有必要开一个的。话说回来，还是应该做些调查研究加上考量自己的情况。

A machine that has Tensorflow, Theano, Torch, Keras, and also Caffe installed. Tensorflow, Theano, Torch, and Caffe are deep learning ecosystem/environment. Keras is the python module that I use to build deep learning algorithm architecture.想这个ami机器上有我想用的几个深度学习框架，比如Tensorflow, Theano, Torch, and Caffe。还有如果有keras，python的一个构建深度学习／机器学习的包。